Replicate this research

An open invitation to falsify, not to agree.

This project measures Bitcoin's long-lived resource costs and how much of them the fee market internalizes. Its value depends entirely on whether someone else can reproduce the numbers. So: please try to break them.

What to replicate

Target	Reproduce in	Artifact
SCCR (the headline metric)	~15 minutes	`research/reproduce/` — JS, Python, C + notebook
UTXO / chain state	minutes, on any synced node	`gettxoutsetinfo` via `tools/research/utxo_state_measure.py`
Production-cost scenarios	minutes	`tools/research/production_cost_ratio.py`
Verification-burden trend	minutes	`tools/research/verify_cost_index.py`
SCCR sensitivity / CI	seconds	`tools/research/sccr_sensitivity.py`

Quickstart: reproduce the SCCR

git clone https://github.com/prateekposwal/block-space-economics
cd block-space-economics/research/reproduce
python3 reproduce_sccr.py --input input/fee_history_capture.json
bash cross_check.sh          # JS = Python = C? prints the verdict

Expected: avg SCCR 0.2406 (min 0.0490, max 1.0757, 153/155 below 1×), with the three implementations agreeing to < 1e-6. If you do not get that, we want to know — that is the useful result.

What would falsify the claims

An SCCR materially different from the published value on the same frozen input.
A node-count N outside ~10K–100K (which would move the level; the direction survives, since SCCR is inverse-linear in N).
A measured UTXO/state size that contradicts the observed row or the reconstructed table by more than the stated grade allows.
A fee-market episode in which fees cover storage at a materially higher rate than the published series, sustained.

How to send a result

Open an issue at github.com/prateekposwal/block-space-economics with: the artifact you ran, your inputs, your output, and your environment. Corrections are published in the Research Changelog with their cause — the project's own 10× correction is precedent that this is the intended path, not an embarrassment.

The standard

Every number is classified Observed / Reconstructed / Modelled with a grade in the Evidence Matrix.
Methods are in the Methodology paper.
The checks that guard the data run in tools/research/integrity_audit.py and gate every change.

If a published number cannot be reproduced from the repository, that is a defect in the repository, and we will treat it as one.

Data source: research/reproduce/output/reproduce_sccr_python.json — deterministic, frozen-input instrument; re-runnable offline.

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